BIOMEDICAL APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING
Keywords:
Artificial Intelligenc, Medical Imaging, Deep Learning, Convolutional Neural Networks (CNNs), Radiomics, Disease Detection, Biomedical Engineering, Image SegmentationAbstract
Artificial Intelligence (AI) has rapidly emerged as a transformative force in biomedical engineering, particularly in medical imaging. This research article explores the diverse applications of AI in the analysis and interpretation of complex medical images, which traditionally rely on the expertise of radiologists. AI techniques, such as machine learning, deep learning, convolutional neural networks (CNNs) and radiomics, have demonstrated high accuracy in tasks such as image classification, lesion segmentation, disease detection and prognostic prediction. Case studies across various diseases, such as brain tumors, breast cancer, diabetic retinopathy and COVID-19, highlight the growing role of AI in enhancing diagnostic performance, reducing time delays and improving patient care. This article also discusses the advantages, limitations and ethical challenges of AI integration into clinical workflows. This further emphasizes the need for explainable AI, data quality and regulatory frameworks to ensure safe and effective deployment. With ongoing advancements, AI has the potential to revolutionize personalized medicine and enable more efficient and equitable healthcare delivery
